diff --git a/notebooks/seaborn_TP.ipynb b/notebooks/seaborn_TP.ipynb
index 0ef1a6f9139e8144277ef7843f6694e8997ecece..47d0a4cd17f11553b2c66be47dfade3d2378c20e 100644
--- a/notebooks/seaborn_TP.ipynb
+++ b/notebooks/seaborn_TP.ipynb
@@ -2,10 +2,10 @@
"cells": [
{
"cell_type": "markdown",
- "id": "rotary-designation",
+ "id": "instrumental-personal",
"metadata": {},
"source": [
- "# <center>**TP**</center>\n",
+ "# <center><b>Hands-on</b></center>\n",
"\n",
"<div style=\"text-align:center\">\n",
" <img src=\"images/seaborn.png\" width=\"600px\">\n",
@@ -21,106 +21,392 @@
},
{
"cell_type": "markdown",
- "id": "respected-history",
+ "id": "compliant-basis",
"metadata": {},
"source": [
"Practice your graphing skills using data from milieu intérieur in `data/mi.csv`:"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "departmental-exhibition",
+ "metadata": {},
+ "source": [
+ "- Do a boxplot showing the differences in temperature between females and males:"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
- "id": "adolescent-spirituality",
+ "id": "98e904b6-6e90-4c74-a463-2339d3961250",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
- "id": "widespread-rendering",
+ "id": "portuguese-worse",
"metadata": {},
"source": [
- "- Do a boxplot showing the differences in temperature between females and males:"
+ "- Using a histogram and continuous probability density curve, display the distribution of age in the dataset"
]
},
{
"cell_type": "code",
"execution_count": null,
- "id": "dressed-performer",
+ "id": "55756807-e1fb-4fb5-878c-5e46acea7a11",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
- "id": "acute-debut",
+ "id": "prepared-stephen",
"metadata": {},
"source": [
- "- Using an histogram, display the distribution of age in the dataset (with kde as well)"
+ "- Use a barplot to show the count of vaccinated for yellow fever (see the documentation for a countplot)"
]
},
{
"cell_type": "code",
"execution_count": null,
- "id": "crucial-bracelet",
+ "id": "1425046c-a058-45fe-95b5-5eca6ebbd33a",
"metadata": {},
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "id": "immediate-method",
+ "metadata": {},
+ "source": [
+ "- Plot the distribution of age for the people vaccinated for the flu"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
- "id": "minor-secretariat",
+ "id": "d567194c-3698-44c9-b5f8-b8a3d3493b0c",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
- "id": "processed-diameter",
+ "id": "temporal-synthesis",
"metadata": {},
"source": [
- "- Use a barplot to show the count of vaccinated for yellow fever (see the documentation for a countplot)"
+ "- Feel free to explore more of [seaborn](https://seaborn.pydata.org/examples/index.html) !"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "db56d49a-4770-4f9e-af6b-78960574d338",
+ "metadata": {},
+ "source": [
+ "# Exploring count matrices from RNA-seq data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5377668b-dea5-4c20-8249-5266f98774eb",
+ "metadata": {},
+ "source": [
+ "<img src=\"images/rnaseq.png\" style=\"margin:0 auto;width:800px\">"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ebf1606b-0b21-4821-a899-551ec33c977e",
+ "metadata": {},
+ "source": [
+ "- Import the count_matrix tsv file from the data folder"
]
},
{
"cell_type": "code",
"execution_count": null,
- "id": "indian-response",
+ "id": "eb53a1f5-9ea7-491e-bcfa-820cb1663af5",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
- "id": "scenic-adoption",
+ "id": "c80d9947-9ccf-4499-a1c2-9194377cd054",
"metadata": {},
"source": [
- "- Plot the distribution of age for the people vaccinated for the flu"
+ "- Simplify the dataframe to only have the \"Geneid\", \"WTx\" and \"Cx\" columns"
]
},
{
"cell_type": "code",
"execution_count": null,
- "id": "weighted-terrain",
+ "id": "56e90032-75ce-47b5-9cd3-95219cd7b26e",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
- "id": "operating-union",
+ "id": "eb65b51f-f689-4a66-b47c-e79f0e9eba52",
"metadata": {},
"source": [
- "- Feel free to explore more of [seaborn](https://seaborn.pydata.org/examples/index.html) !"
+ "- Format properly your DataFrame to be able to use https://seaborn.pydata.org/generated/seaborn.clustermap.html to realize a heatmap."
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9b422fcb-7cc1-4766-92e3-276742381ae6",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f8d6188e-3a37-4ba5-b377-a11696054e9c",
+ "metadata": {},
+ "source": [
+ "- Explore the clustermap documentation to have a more visual heatmap by standardizing the data within genes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "06be3f98-2167-44ac-9318-955286d77903",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2e61a207-223a-4c01-88ea-76b1b8c3a0b9",
+ "metadata": {},
+ "source": [
+ "- Reformat the counts_df dataframe to have genes in columns and samples in rows.\n",
+ "- Add a \"group\" column defining the grouping of the samples:\n",
+ " - \"WTx\" samples will be from the \"WT\" group.\n",
+ " - \"Cx\" samples will be from the \"C\" group."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "eea3f521-6960-44ab-ac0b-fcf5a002237f",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9a88ecb1-9ed3-4160-91ee-24a30e994b71",
+ "metadata": {},
+ "source": [
+ "- Display a barplot showing the mean expression for each group for a particular gene (for example \"gene-LEPBI_RS00065\")."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "cf74e85e-eef3-4023-bb88-5a864cf3c3f9",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "99e2455a-cb7d-44d5-a4a0-2cf272c814ab",
+ "metadata": {},
+ "source": [
+ "- Try plotting a swarmplot on top of the previous barplot:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7cf225f9-aea7-4cd9-ac90-a99592799527",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d200d375-362e-4c1d-a88e-130b094e6feb",
+ "metadata": {},
+ "source": [
+ "- Now plot the same data using a boxplot. Can you see the problem of displaying boxplots for this kind of data ?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e4daf00e-9a2c-4ec4-9d26-aa18aae5d82d",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2e1cabe0-aab7-4f0e-888e-81aae7d5df8d",
+ "metadata": {},
+ "source": [
+ "- Compute the median of each genes by groups:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6ffd0f59-0fd7-41b9-a87a-c6e1a74145e8",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "308cc10b-6727-4bc5-b05d-4777037e252e",
+ "metadata": {},
+ "source": [
+ "We are going now to add extra annotations to this median table in order to identify genes of interest.\n",
+ "- Import the annotation.csv table from the data folder: "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9be6ee5b-d497-47fa-8ac5-cf5514fd52c0",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "50fa81a7-3f34-4160-ad2d-f77d21be9ac0",
+ "metadata": {},
+ "source": [
+ "Annotations in this table are available for many types of loci (the \"genetic_type\" column), but here we will focus on the \"gene\" genetic_type. \n",
+ "- Filter the annotation dataframe to have only \"gene\" as \"genetic_type\"."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f9a8bcf7-0bcc-43e8-828a-ec204658e528",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f8a4e744-e7e2-43b6-b3d4-e59feb40d3ff",
+ "metadata": {},
+ "source": [
+ "- Concatenate the dataframe with median by group and the annotation dataframe together:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "afd8467a-33e1-4b9e-8f6d-b2229099c874",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "af9f8e1f-5f8b-4152-b08a-44e957f13cec",
+ "metadata": {},
+ "source": [
+ "- Calculate an estimate of the gene expression fold change for each gene (by dividing the C median expressions by WT median expressions).\n",
+ "- Add it as a \"FoldChange\" column to the previous dataframe."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bb617d00-2c2d-45cc-ace0-3656dc999b17",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d70eb26b-0a26-4bbc-af03-ba8781b09fb5",
+ "metadata": {},
+ "source": [
+ "- Use a barplot to display fold changes and using the new gene annotation (The \"Name\" column)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4dd4cbee-547f-43f1-9ed7-173f3040b8d5",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "34a26492-7c6b-4a07-a4de-67ec8f693cdc",
+ "metadata": {},
+ "source": [
+ "- By calculating the length of each gene and using a visualisation, does gene expression appears correlated with gene length ?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6f35b696-0807-4df4-9310-cb9197e7bf85",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a2627322-e6a5-422f-8a69-b89dbd4b777e",
+ "metadata": {},
+ "source": [
+ "- Create a function which produce a single image with four different plots of your choice and save it to pdf file."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "70e001a1-2848-4fb7-9f33-7beb4475e0fc",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0d05aba4-3c85-4cd9-85f3-5296b19308fb",
+ "metadata": {},
+ "source": [
+ "# Extras"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "66d6668e-683f-462e-a72f-28bdda8736f2",
+ "metadata": {},
+ "source": [
+ "- Using ipywidget, make a function to display barplot of gene expression by groups with the gene being selected by the user (using a Dropdown widget for example)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e587f202-7ca4-43fb-ac3c-015c740c69d2",
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3 (ipykernel)",
+ "display_name": "Python [conda env:dev]",
"language": "python",
- "name": "python3"
+ "name": "conda-env-dev-py"
},
"language_info": {
"codemirror_mode": {
@@ -132,7 +418,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.10"
+ "version": "3.10.4"
}
},
"nbformat": 4,